This file outlines the sampling strategy for the Punjab province of Pakistan.
The survey is a combined effort of the Early Learning Partnership project and of the Global Education Policy Dashboard project.
Overall, we draw a sample of 200 public schools, 200 private schools and 200 public-private partnership (PPP) schools. We stratified by urban/rural.
At this stage it is important to note, that there are certain districts which we may not be able to visit due to security concerns, these are:
We have removed these districts from the sampling frame.
Out of the 200 public schools to be surveyed we would like approximately 100 of these schools to be schools that are meeting ECE quality standards (in the data set this corresponds to public_strata==1). Out of the remaining public schools to be sampled, 50 schools will be schools that have ECE but do not meet quality standards (public_strata==2) and 50 will be schools that have no ECE at all, and have only have katchi classes (public_strata==3).
Due to operational constraints, we did not draw a random sample of all schools at province level. We selected six districts for the survey (out of 32). The survey team drew a convenience sample of 6 districts that is representative of North, Central and South Punjab, which includes both richer and poorer districts. A convenience sample was appropriate due to security and operational constraints of working in Punjab. The selected districts were:
In order to deal with potential refusals and closed schools, a set of replacement schools was also drawn. Within the final strata, schools were sampled proportional to size (number of total enrolled children in pre-primary).
Our sampling frame then consists of public, private, and PPP schools in these six districts. Additionally, we restricted the frame to schools with at least 10 children enrolled in pre-primary, have at least 3 students in grade 1, and at least 3 students in grades 3, 4, or 5. These latter two restrictions ensured that the schools would contain relevant students and teachers for the Global Education Policy Dashboard survey.
| variable | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|
| Public Schools | ||||||||
| num_teachers | 4.51 | 1.62 | 1 | 4 | 4 | 5 | 21 | ▂▇▁▁▁▁▁▁ |
| rural | 0.86 | 0.35 | 0 | 1 | 1 | 1 | 1 | ▂▁▁▁▁▁▁▇ |
| total_1st_enrollment | 34.83 | 26.43 | 3 | 18 | 29 | 43 | 399 | ▇▁▁▁▁▁▁▁ |
| total_1st_enrollment_boys | 17.65 | 18.17 | 0 | 6 | 13 | 24 | 217 | ▇▂▁▁▁▁▁▁ |
| total_1st_enrollment_girls | 17.19 | 21.01 | 0 | 4 | 11 | 22 | 340 | ▇▁▁▁▁▁▁▁ |
| total_ece_enrollment | 14.71 | 12.31 | 0 | 6 | 10.5 | 20 | 85 | ▇▅▂▁▁▁▁▁ |
| total_ece_enrollment_boys | 7.08 | 7.75 | 0 | 2 | 5 | 10 | 44 | ▇▃▂▁▁▁▁▁ |
| total_ece_enrollment_gils | 7.64 | 9.64 | 0 | 1 | 5 | 10 | 80 | ▇▂▁▁▁▁▁▁ |
| total_enrollment | 148.56 | 93.6 | 12 | 90 | 127 | 183 | 1322 | ▇▂▁▁▁▁▁▁ |
| total_katchi_enrollment | 54.15 | 39.19 | 0 | 30 | 46 | 68 | 589 | ▇▂▁▁▁▁▁▁ |
| total_katchi_enrollment_boys | 28.44 | 26.37 | 0 | 11 | 23 | 38 | 386 | ▇▁▁▁▁▁▁▁ |
| total_katchi_enrollment_girls | 25.71 | 28.19 | 0 | 7 | 19 | 35 | 528 | ▇▁▁▁▁▁▁▁ |
| Private Schools | ||||||||
| num_teachers | 14.8 | 13.62 | 2 | 8 | 12 | 17 | 299 | ▇▁▁▁▁▁▁▁ |
| rural | 0.47 | 0.5 | 0 | 0 | 0 | 1 | 1 | ▇▁▁▁▁▁▁▇ |
| total_1st_enrollment | 28.84 | 25.96 | 3 | 14 | 22 | 35 | 502 | ▇▁▁▁▁▁▁▁ |
| total_1st_enrollment_boys | 15.39 | 14.41 | 1 | 7 | 12 | 19 | 301 | ▇▁▁▁▁▁▁▁ |
| total_1st_enrollment_girls | 13.42 | 12.91 | 1 | 6 | 10 | 16 | 232 | ▇▁▁▁▁▁▁▁ |
| total_enrollment | 247.15 | 240.43 | 28 | 123 | 185 | 290 | 5641 | ▇▁▁▁▁▁▁▁ |
| total_pre_primary_enrollment | 79.64 | 58.72 | 10 | 44 | 66 | 98 | 1026 | ▇▁▁▁▁▁▁▁ |
| total_pre_primary_enrollment_boys | 46.94 | 34.58 | 4 | 27 | 39 | 57 | 568 | ▇▁▁▁▁▁▁▁ |
| total_pre_primary_enrollment_girls | 41.86 | 30.18 | 4 | 23 | 35 | 51 | 458 | ▇▂▁▁▁▁▁▁ |
Schools (PSUs) will be selected using the Probability Proportional to Size (PPS) sampling method, where size is based on the total pre-primary enrollment of the schools. This method allows schools with larger enrollment of to have a higher chance of being selected in the sample. It is most useful when the sampling units vary considerably in size because it assures that those in larger sites have the same probability of getting into the sample as those in smaller sites, and vice versa.
Out of the 200 public schools to be surveyed we would like approximately 100 of these schools to be schools that are meeting ECE quality standards (in the data set this corresponds to public_strata==1). Out of the remaining public schools to be sampled, 50 schools will be schools that have ECE but do not meet quality standards (public_strata==2) and 50 will be schools that have no ECE at all, and have only have katchi classes (public_strata==3).
Around 86% of schools in the public school sampling frame are classified as rural. We do stratification by urban rural status, and over-sample urban schools, so that we have adequate power to detect differences. This results in a sample of around 64 urban schools and 136 rural schools.
Units are allocated to our districts proportionate to the size of the pre-primary enrollment in the district.
## Adding missing grouping variables: `public_strata`Out of the 200 private schools to be surveyed we will stratify by district and by the urban/rural status of the school. Roughly 50% of the sample will be urban, compared to 47% in the full private sampling frame.
Below is a list of replacement schools for each sampled school. Replacement schools were randomly selected among the set of schools in the tehsil of the same urban/rural status, not including the orginally sampled schools. Each row contains the school name, location, and other information for each replacement school. In the final 5 columns of the database is the school code, school name, district, and tehsil of the originally sampled school for which this school serves as a replacement.
| variable | mean | sd | p0 | p25 | p50 | p75 | p100 | complete | hist |
|---|---|---|---|---|---|---|---|---|---|
| Public Schools | |||||||||
| num_teachers | 5.57 | 2.39 | 2 | 4 | 5 | 6.75 | 19 | 86 | ▇▇▃▂▁▁▁▁ |
| rural | 0.68 | 0.47 | 0 | 0 | 1 | 1 | 1 | 200 | ▃▁▁▁▁▁▁▇ |
| total_1st_enrollment | 60.56 | 49.61 | 7 | 32 | 47 | 68.5 | 399 | 200 | ▇▃▁▁▁▁▁▁ |
| total_1st_enrollment_boys | 27.68 | 30.12 | 0 | 8 | 20 | 39 | 198 | 200 | ▇▅▂▁▁▁▁▁ |
| total_1st_enrollment_girls | 32.88 | 39.6 | 0 | 7 | 21.5 | 46 | 340 | 200 | ▇▂▁▁▁▁▁▁ |
| total_ece_enrollment | 16.93 | 12.1 | 0 | 7.5 | 15 | 24 | 51 | 95 | ▇▇▅▆▃▂▁▁ |
| total_ece_enrollment_boys | 7.38 | 8.57 | 0 | 2 | 5 | 10.5 | 44 | 95 | ▇▂▂▁▁▁▁▁ |
| total_ece_enrollment_gils | 9.55 | 10.81 | 0 | 2 | 6 | 10.5 | 49 | 95 | ▇▅▂▁▁▁▁▁ |
| total_enrollment | 229.78 | 142.44 | 53 | 126.25 | 206.5 | 270 | 885 | 90 | ▇▇▃▂▁▁▁▁ |
| total_katchi_enrollment | 88.83 | 56.58 | 8 | 51.75 | 74 | 106.5 | 343 | 200 | ▃▇▃▂▁▁▁▁ |
| total_katchi_enrollment_boys | 43.02 | 37.5 | 0 | 16 | 37 | 62 | 288 | 200 | ▇▆▂▁▁▁▁▁ |
| total_katchi_enrollment_girls | 45.81 | 47.5 | 0 | 14.75 | 33.5 | 66 | 310 | 200 | ▇▅▁▁▁▁▁▁ |
| Private Schools | |||||||||
| num_teachers | 19.05 | 16.37 | 4 | 10 | 14 | 22.25 | 120 | 118 | ▇▂▁▁▁▁▁▁ |
| rural | 0.49 | 0.5 | 0 | 0 | 0 | 1 | 1 | 200 | ▇▁▁▁▁▁▁▇ |
| total_1st_enrollment | 39.01 | 34.23 | 5 | 19 | 28 | 45.5 | 219 | 199 | ▇▃▁▁▁▁▁▁ |
| total_1st_enrollment_boys | 21.41 | 19.38 | 2 | 10 | 16 | 23.5 | 128 | 200 | ▇▅▁▁▁▁▁▁ |
| total_1st_enrollment_girls | 17.83 | 16.2 | 1 | 7 | 13 | 20.5 | 98 | 199 | ▇▃▁▁▁▁▁▁ |
| total_enrollment | 347.33 | 317.02 | 54 | 172.75 | 239 | 399.25 | 2618 | 200 | ▇▂▁▁▁▁▁▁ |
| total_pre_primary_enrollment | 113.92 | 81.12 | 14 | 61 | 91 | 124.25 | 468 | 200 | ▆▇▂▁▁▁▁▁ |
| total_pre_primary_enrollment_boys | 64.46 | 47.8 | 15 | 34 | 50.5 | 73.25 | 261 | 140 | ▇▆▂▁▁▁▁▁ |
| total_pre_primary_enrollment_girls | 55.26 | 35.4 | 10 | 33 | 44 | 62.5 | 207 | 140 | ▅▇▂▂▁▁▁▁ |